8000 DOC remove tutorials (#29104) · scikit-learn/scikit-learn@65c00c7 · GitHub
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adrinjalaliCharlie-XIAO
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DOC remove tutorials (#29104)
Co-authored-by: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com>
1 parent 1009ca4 commit 65c00c7

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‎doc/conf.py

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@@ -448,6 +448,7 @@ def add_js_css_files(app, pagename, templatename, context, doctree):
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"auto_examples/exercises/plot_cv_digits.py": (
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"auto_examples/model_selection/plot_nested_cross_validation_iris.py"
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),
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"tutorial/machine_learning_map/index.html": "machine_learning_map/index.html",
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}
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html_context["redirects"] = redirects
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for old_link in redirects:

‎doc/conftest.py

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@@ -126,10 +126,6 @@ def pytest_runtest_setup(item):
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setup_rcv1()
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elif fname.endswith("datasets/twenty_newsgroups.rst") or is_index:
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setup_twenty_newsgroups()
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elif (
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fname.endswith("tutorial/text_analytics/working_with_text_data.rst") or is_index
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):
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setup_working_with_text_data()
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elif fname.endswith("modules/compose.rst") or is_index:
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setup_compose()
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elif fname.endswith("datasets/loading_other_datasets.rst"):
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rst_files_requiring_matplotlib = [
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"modules/partial_dependence.rst",
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"modules/tree.rst",
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"tutorial/statistical_inference/settings.rst",
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"tutorial/statistical_inference/supervised_learning.rst",
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]
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for each in rst_files_requiring_matplotlib:
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if fname.endswith(each):

‎doc/developers/contributing.rst

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@@ -650,10 +650,8 @@ We are glad to accept any sort of documentation:
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* **User guide:** These provide more detailed information about the algorithms
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implemented in scikit-learn and generally live in the root
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`doc/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc>`_ directory
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and `doc/modules/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc/modules>`_.
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* **Tutorials:** These introduce various statistical learning and machine learning
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concepts and are located in
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`doc/tutorial <https://github.com/scikit-learn/scikit-learn/tree/main/doc/tutorial>`_.
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and
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`doc/modules/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc/modules>`_.
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* **Examples:** These provide full code examples that may demonstrate the use
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of scikit-learn modules, compare different algorithms or discuss their
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interpretation, etc. Examples live in

‎doc/getting_started.rst

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@@ -53,6 +53,8 @@ new data. You don't need to re-train the estimator::
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>>> clf.predict([[4, 5, 6], [14, 15, 16]]) # predict classes of new data
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array([0, 1])
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You can check :ref:`ml_map` on how to choose the right model for your use case.
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Transformers and pre-processors
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-------------------------------
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@@ -227,6 +229,3 @@ provide. You can also find an exhaustive list of the public API in the
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You can also look at our numerous :ref:`examples <general_examples>` that
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illustrate the use of ``scikit-learn`` in many different contexts.
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The :ref:`tutorials <tutorial_menu>` also contain additional learning
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resources.

‎doc/tutorial/machine_learning_map/README.md renamed to ‎doc/images/ml_map.README.rst

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‎doc/index.rst.template

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auto_examples/index
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Community <https://blog.scikit-learn.org/>
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getting_started
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Tutorials <tutorial/index>
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whats_new
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Glossary <glossary>
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Development <{{ development_link }}>

‎doc/tutorial/machine_learning_map/index.rst renamed to ‎doc/machine_learning_map.rst

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@@ -14,7 +14,7 @@ problems with regard to which estimators to try on your data. Click on any estim
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the chart below to see its documentation. The 😭 emoji is to be read as "if this
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estimator does not achieve the desired outcome, then follow the arrow and try the next
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one". Use scroll wheel to zoom in and out, and click and drag to pan around. You can
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also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`.
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also download the chart: :download:`ml_map.svg <images/ml_map.svg>`.
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.. raw:: html
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@@ -36,7 +36,7 @@ also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`.
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}
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</style>
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<script src="../../_static/scripts/vendor/svg-pan-zoom.min.js"></script>
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<script src="_static/scripts/vendor/svg-pan-zoom.min.js"></script>
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<script>
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document.addEventListener("DOMContentLoaded", function () {
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const beforePan = function (oldPan, newPan) {
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<div id="sk-ml-map">
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.. raw:: html
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:file: ../../images/ml_map.svg
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:file: images/ml_map.svg
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.. raw:: html
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‎doc/presentations.rst

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External Resources, Videos and Talks
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===========================================
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For written tutorials, see the :ref:`Tutorial section <tutorial_menu>` of
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the documentation.
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New to Scientific Python?
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==========================
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For those that are still new to the scientific Python ecosystem, we highly
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An extensive tutorial, consisting of four sessions of one hour.
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The tutorial covers the basics of machine learning,
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many algorithms and how to apply them using scikit-learn. The
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material corresponding is now in the scikit-learn documentation
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section :ref:`stat_learn_tut_index`.
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many algorithms and how to apply them using scikit-learn.
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- `Statistical Learning for Text Classification with scikit-learn and NLTK
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<https://pyvideo.org/video/417/pycon-2011--statistical-machine-learning-for-text>`_

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